


"""simple docstring"""



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_							=							logging.get_logger()







@dataclass
class    __a						:
				lowerCamelCase     :       nn.Module
				lowerCamelCase     :       List[nn.Module]			       =field(default_factory=__snake_case							)
				lowerCamelCase     :       list			       =field(default_factory=__snake_case							)







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			len(list(m.modules()   )   ) == 1 or isinstance(UpperCAmelCase      ,	nn.Convad   ) or isinstance(UpperCAmelCase      ,	nn.BatchNormad   )
					if has_not_submodules:
						self.traced.append(UpperCAmelCase   )







				def __call__( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook   )   )
					self.module(UpperCAmelCase   )
					[x.remove() for x in self.handles]
					return self







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return list(filter(lambda UpperCAmelCase   : len(list(x.state_dict().keys()   )   ) > 0      ,	self.traced   )   )







@dataclass
class    __a						:
				lowerCamelCase     :       nn.Module
				lowerCamelCase     :       nn.Module
				lowerCamelCase     :       int			       =0
				lowerCamelCase     :       List			       =field(default_factory=__snake_case							)
				lowerCamelCase     :       List			       =field(default_factory=__snake_case							)







				def __call__( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			Tracker(self.dest   )(UpperCAmelCase   ).parametrized
					lowerCAmelCase_      							=			Tracker(self.src   )(UpperCAmelCase   ).parametrized

					lowerCAmelCase_      							=			list(filter(lambda UpperCAmelCase   : type(UpperCAmelCase   ) not in self.src_skip      ,	UpperCAmelCase   )   )
					lowerCAmelCase_      							=			list(filter(lambda UpperCAmelCase   : type(UpperCAmelCase   ) not in self.dest_skip      ,	UpperCAmelCase   )   )

					if len(UpperCAmelCase   ) != len(UpperCAmelCase   ):
						raise Exception(
						    F"""Numbers of operations are different. Source module has {len(UpperCAmelCase   )} operations while"""
						    F""" destination module has {len(UpperCAmelCase   )}."""   )

					for dest_m, src_m in zip(UpperCAmelCase      ,	UpperCAmelCase   ):
						dest_m.load_state_dict(src_m.state_dict()   )
						if self.verbose == 1:
							print(F"""Transfered from={src_m} to={dest_m}"""   )




def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   ResNetConfig			,   _lowercase :   Path			,   _lowercase :   bool = True	)						-> Tuple:


	"""simple docstring"""




	print(F"""Converting {name}..."""	)
	with torch.no_grad():
		lowerCAmelCase_      							=			timm.create_model(_lowercase			,   pretrained=_lowercase	).eval()
		lowerCAmelCase_      							=			ResNetForImageClassification(_lowercase	).eval()
		lowerCAmelCase_      							=			ModuleTransfer(src=_lowercase			,   dest=_lowercase	)
		lowerCAmelCase_      							=			torch.randn((1, 3, 2_2_4, 2_2_4)	)
		module_transfer(_lowercase	)

	assert torch.allclose(from_model(_lowercase	)			,   our_model(_lowercase	).logits	), "The model logits don't match the original one."

	lowerCAmelCase_      							=			F"""resnet{"-".join(name.split("resnet"	)	)}"""
	print(_lowercase	)

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name			,   commit_message='''Add model'''			,   use_temp_dir=_lowercase			,   )

		# we can use the convnext one
		lowerCAmelCase_      							=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name			,   commit_message='''Add image processor'''			,   use_temp_dir=_lowercase			,   )

		print(F"""Pushed {checkpoint_name}"""	)
def      UpperCAmelCase     (       _lowercase :   Path			,   _lowercase :   str = None			,   _lowercase :   bool = True	)						-> Any:


	"""simple docstring"""




	lowerCAmelCase_      							=			'''imagenet-1k-id2label.json'''
	lowerCAmelCase_      							=			1_0_0_0
	lowerCAmelCase_      							=			(1, num_labels)

	lowerCAmelCase_      							=			'''huggingface/label-files'''
	lowerCAmelCase_      							=			num_labels
	lowerCAmelCase_      							=			json.load(open(hf_hub_download(_lowercase			,   _lowercase			,   repo_type='''dataset'''	)			,   '''r'''	)	)
	lowerCAmelCase_      							=			{int(_lowercase	): v for k, v in idalabel.items()}

	lowerCAmelCase_      							=			idalabel
	lowerCAmelCase_      							=			{v: k for k, v in idalabel.items()}

	lowerCAmelCase_      							=			partial(_lowercase			,   num_labels=_lowercase			,   idalabel=_lowercase			,   labelaid=_lowercase	)

	lowerCAmelCase_      							=			{
	    '''resnet18''': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]			,   hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]			,   layer_type='''basic'''	),
	    '''resnet26''': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]			,   hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,   layer_type='''bottleneck'''	),
	    '''resnet34''': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]			,   hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]			,   layer_type='''basic'''	),
	    '''resnet50''': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]			,   hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,   layer_type='''bottleneck'''	),
	    '''resnet101''': ImageNetPreTrainedConfig(
	        depths=[3, 4, 2_3, 3]			,   hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,   layer_type='''bottleneck'''	),
	    '''resnet152''': ImageNetPreTrainedConfig(
	        depths=[3, 8, 3_6, 3]			,   hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,   layer_type='''bottleneck'''	),
	}

	if model_name:
		convert_weight_and_push(_lowercase			,   names_to_config[model_name]			,   _lowercase			,   _lowercase	)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase	)
	return config, expected_shape


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default=None,
		    type=str,
		    help=(
		        'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
		        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=Path,
		    required=True,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument(
		    '--push_to_hub',
		    default=True,
		    type=bool,
		    required=False,
		    help='If True, push model and image processor to the hub.',
		)

		lowercase_							=							parser.parse_args()
		lowercase_							=							args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



"""simple docstring"""



from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class    __a						:
				pass